Screening for Cognitive Impairment in Movement Disorders: Comparison of the Montreal Cognitive Assessment and Quick Mild Cognitive Impairment Screen in Parkinson’s Disease and Lewy Body Dementia
Bibliographic record
Abstract
Background: The Montreal Cognitive Assessment (MoCA) is recommended by the Movement Disorder Society for cognitive testing in movement disorders including Parkinson’s disease (PD) and lewy body dementia. Few studies have compared cognitive screening instruments in these diseases, which overlap clinically. Objective: To compare the MoCA and Quick Mild Cognitive Impairment (Q mci) screen in this population. Methods: Patients attending memory and movement disorder clinics associated with a university hospital had the MoCA and Q mci screen performed and diagnostic accuracy compared with the area under the receiver operating characteristic curve (AUC). Duration and severity of movement disorders was assessed using the Unified PD Rating Scale (UPDRS). Results: In total, 133 assessments were available, median age 74±5. Median education was 11±4 years and 65% were male. Median total UPDRS score was 37±26. Median Q mci screen was 51±27, median MoCA was 19±10. There were statistically significant differences in test scores between those with subjective symptoms but normal cognition, mild cognitive impairment (MCI) and dementia ( p < 0.001). The Q mci screen had significantly greater accuracy differentiating normal cognition from MCI versus the MoCA (AUC 0.90 versus 0.72, p = 0.01). Both instruments had similar accuracy in identifying cognitive impairment and separating MCI from dementia. The median administration time for the Q mci screen and MoCA were 5.19 and 9.24 minutes ( p < 0.001), respectively. Conclusions: Both the MoCA and Q mci screen have good to excellent accuracy in a population with movement disorders experiencing cognitive symptoms. The Q mci screen was significantly more accurate for those with early symptoms and had a shorter administration time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".